700.385 (24S) Lab of Autonomous Driving Cars
Überblick
- Lehrende/r
- LV-Titel englisch Lab of Autonomous Driving Cars
- LV-Art Kurs (prüfungsimmanente LV )
- LV-Modell Onlinelehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 14 (12 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 05.03.2024
- eLearning zum Moodle-Kurs
- Seniorstudium Liberale Ja
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Upon completing this course, students will achieve the following comprehensive learning outcomes:
Foundation of Autonomous Driving: Provide students with a solid understanding of the fundamentals of Autonomous Driving, including its historical context and the core components involved.
Technological Background: Explore the underlying technology and principles that drive Autonomous Driving Cars, enabling students to grasp the innovations and advancements in this field.
System Components: Familiarize students with the various essential components of Autonomous Driving Cars, giving them insights into the integral parts that make these vehicles autonomous.
Autonomy Methods Overview: Provide an overview of diverse methodologies used to achieve autonomy in Autonomous Driving Cars, covering sensor fusion, localization, perception, and decision-making algorithms.
Practical Machine Vision and ML: Equip students with practical skills in implementing Machine Vision and Machine Learning algorithms tailored for Autonomous Driving, ensuring hands-on experience.
Simulation and Real-Time Testing: Enable students to simulate and rigorously test algorithms using game engines, generating real-time data to assess and refine autonomous systems effectively.
Lehrmethodik inkl. Einsatz von eLearning-Tools
- Interactive Lectures and presentationon the topic with examples.
- Practical Implementation.
- In-class activities and discussions
- Assignments
- Online Resources on Moodle
- Collaborative Projects
- CARLA Simulator
Inhalt/e
1. Theoretical Understanding of Autonomous Vehicles:
- Gain familiarity with the various levels of Self-Driving Cars.
- Acquire in-depth knowledge of the underlying technology behind Autonomous Driving Cars.
- Develop insights into the diverse applications of Autonomous Driving Cars across various industries.
- Master control, path planning, and tracking techniques relevant to Autonomous Driving.
- Attain expertise in Image Processing as applied to Self-Driving Cars.
2. Proficiency in Deep Learning and Machine Vision for Autonomous Driving Cars:
- Learn Semantic Segmentation techniques for analyzing scenes effectively.
- Understand Lane Detection algorithms to ensure safe driving.
- Gain proficiency in Object Detection methods for identifying obstacles and other vehicles.
- Master steering control strategies for precise vehicle maneuvering.
3. Practical Application using the CARLA Simulator:
- Apply the acquired knowledge practically by utilizing the CARLA simulator.
- Implement machine learning techniques and computer vision to simulate and assess Autonomous Driving Cars in a virtual environment.
- Gain hands-on experience that enhances your ability to develop and evaluate autonomous systems effectively.
Erwartete Vorkenntnisse
Prior Knowledge Expected:
- Knowledge of Python programming.
- Background in Deep Learning and Machine Vision.
- Ability to work with COLAB or Jupyter notebook for implementation.
Recommended Courses:
- Fundamentals of Image Processing
- Machine Learning in Intelligent Transportation
- Practical Introduction to Neural Networks and Deep Learning
- Artificial Vision
- Tutorium in Machine Learning, TensorFlow, PyTorch Basics
Curriculare Anmeldevoraussetzungen
none
Literatur
Will be uploaded on Moodle
Prüfungsinformationen
Prüfungsmethode/n
Assignments:
- Homeworks
- In-class activities
- Homework presentations
Group Presentations:
- Presentation on selected topics of Autonomous Driving Cars (Week 7 or Week 8)
Final Project
Prüfungsinhalt/e
- Understanding Autonomous Cars
- Utilizing simple tasks in Computer Vision and Machine learning for Autonomous Driving Cars.
- Using the CARLA Simulator with implemented topics thoughout the semester
Beurteilungskriterien/-maßstäbe
Understanding of the Topic:
- Proper comprehension of course material.
Homework and Final Project Implementation:
- Effective completion of implementation tasks.
Active Learning Throughout the Semester:
- Active participation and engagement in class activities.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
- 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: ICE- Supplements
(Wahlfach)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
- 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: ICE- Supplements
(Wahlfach)
- Bachelorstudium Robotics and Artificial Intelligence
(SKZ: 295, Version: 22W.1)
-
Fach: Robotics & AI Applications
(Wahlfach)
-
8.1 Robotics & AI Applications (
0.0h VO, VC, UE, KS / 12.0 ECTS)
- 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
-
8.1 Robotics & AI Applications (
0.0h VO, VC, UE, KS / 12.0 ECTS)
-
Fach: Robotics & AI Applications
(Wahlfach)